Mark E. Borsuk

Selecting among five common modelling approaches for integrated environmental assessment and management

The design and implementation of effective environmental policies need to be informed by a holistic understanding of the system processes (biophysical, social and economic), their complex interactions, and how they respond to various changes. Models, integrating different system processes into a unified framework, are seen as useful tools to help analyse alternatives with stakeholders, assess their outcomes, and communicate results in a transparent way. This paper reviews five common approaches or model types that have the capacity to integrate knowledge by developing models that can accommodate multiple issues, values, scales and uncertainty considerations, as well as facilitate stakeholder engagement. The approaches considered are: systems dynamics, Bayesian networks, coupled component models, agent-based models and knowledge-based models (also referred to as expert systems). We start by discussing several considerations in model development, such as the purpose of model building, the availability of qualitative versus quantitative data for model specification, the level of spatio-temporal detail required, and treatment of uncertainty. These considerations and a review of applications are then used to develop a framework that aims to assist modellers and model users in the choice of an appropriate modelling approach for their integrated assessment applications and that enables more effective learning in interdisciplinary settings. We review five common integrated modelling approaches.Model choice considers purpose, data type, scale and uncertainty treatment.We present a guiding framework for selecting the most appropriate approach.

On Monte Carlo methods for Bayesian inference

Bayesian methods are experiencing increased use for probabilistic ecological modelling. Most Bayesian inference requires the numerical approximation of analytically intractable integrals. Two methods based on Monte Carlo simulation have appeared in the ecological/environmental modelling literature. Though they sound similar, the Bayesian Monte Carlo (BMC) and Markov Chain Monte Carlo (MCMC) methods are very different in their efficiency and effectiveness in providing useful approximations for accurate inference in Bayesian applications. We compare these two methods using a low-dimensional biochemical oxygen demand decay model as an example. We demonstrate that the BMC is extremely inefficient because the prior parameter distribution, from which the Monte Carlo sample is drawn, is often a poor surrogate for the posterior parameter distribution, particularly if the parameters are highly correlated. In contrast, MCMC generates a chain that converges, in distribution, on the posterior parameter distribution, that can be regarded as a sample from the posterior distribution. The inefficiency of the BMC can lead to marginal posterior parameter distributions that appear irregular and may be highly misleading because the important region of the posterior distribution may never be sampled. We also point out that a priori specification of the model error variance can strongly influence the estimation of the principal model parameters. Although the BMC does not require that the model error variance be specified, most published applications have treated this variance as a known constant. Finally, we note that most published BMC applications have chosen a uniform prior distribution, making the BMC more similar to a likelihood-based inference rather than a Bayesian method because the posterior is unaffected by the prior. Though other prior distributions could be applied, the treatment of Monte Carlo samples with any other choice of prior distribution has not been discussed in the BMC literature.

Long-term changes in watershed nutrient inputs and riverine exports in the Neuse River, North Carolina.

We compared patterns of historical watershed nutrient inputs with in-river nutrient loads for the Neuse River, NC. Basin-wide sources of both nitrogen and phosphorus have increased substantially during the past century, marked by a sharp increase in the last 10 years resulting from an intensification of animal production. However, this recent increase is not reflected in changes in river loading over the last 20 years. Temporal patterns in river loads more closely parallel short-term changes in point sources and cropland nutrient application despite their overall lower magnitude. Total phosphorus loads have declined at all stations considered, corresponding to a 1988 phosphate detergent ban. Nitrogen load temporal patterns vary by location and the nitrogen fraction considered. The furthest upstream station exhibited nitrogen decreases after the completion of a dam in 1983. At a station just downstream of a rapidly growing urban area, the total nitrogen load has increased since the mid-1980s, primarily as a nitrate concentration increase. This is consistent with concurrent increases in chemical fertilizer use and point source discharges, as well as increased nitrification at treatment plants. This increase in nitrate loading is not reflected at the most downstream station, where no clear nitrogen trends are discernable. The lack of clear downstream nutrient increases suggests that current water quality impairment in the lower river and estuary may result from chronic nutrient overload rather than recent changes in the watershed. If this is true, then the impact of a planned 30% nitrogen loading reduction may not be immediately apparent. We calculate that, given annual variability, detecting a load reduction of this magnitude will take at least four years, and, should nutrients accumulated in the watershed become a significant source, detecting the resulting ecological improvements is likely to take substantially longer.

Concepts of decision support for river rehabilitation

Abstract River rehabilitation decisions, like other decisions in environmental management, are often taken by authorities without sufficient transparency about how different goals, predictions, and concerns were considered during the decision making process. This can lead to lack of acceptance or even opposition by stakeholders. In this paper, a concept is outlined for the use of techniques of decision analysis to structure scientist and stakeholder involvement in river rehabilitation decisions. The main elements of this structure are (i) an objectives hierarchy that facilitates and stimulates explicit discussion of goals, (ii) an integrative probability network model for the prediction of the consequences of rehabilitation alternatives, and (iii) a mathematical representation of preferences for possible outcomes elicited from important stakeholders. This structure leads to transparency about expectations of outcomes by scientists and valuations of these outcomes by stakeholders and decision makers. It can be used (i) to analyze synergies and conflict potential between stakeholders, (ii) to analyze the sensitivity of alternative-rankings to uncertainty in prediction and valuation, and (iii) as a basis for communicating the reasons for the decision. These analyses can be expected to support consensus-building among stakeholders and stimulate the creation of alternatives with a greater degree of consensus. Because most decisions in environmental management are characterized by similarly complex scientific problems and diverse stakeholders, the outlined methodology will be easily transferable to other settings.

A bayesian hierarchical model to predict benthic oxygen demand from organic matter loading in estuaries and coastal zones

Ecological models that have a theoretical basis and yet are mathematically simple enough to be parameterized using available data are likely to be the most useful for environmental management and decision-making. Mechanistic foundations improve confidence in model predictions, while statistical methods provide empirical support for parameter selection and allow for estimates of predictive uncertainty. However, even models that are mechanistically simple can be overparameterized when system-specific data are limited. To overcome this problem, models are often fit to data sets composed of observations from multiple systems. The resulting parameter estimates are then used to predict changes within a single system, given changes in management variables. However, the assumption of common parameter values across all systems may not always be valid. This assumption can be relaxed by adopting a hierarchical approach. Under the hierarchical structure, each system has its own set of parameter values, but some commonality in values is assumed across systems. An underlying population distribution is employed to structure this commonality among parameters, thereby avoiding the problems of overfitting. The hierarchical approach is, therefore, a practical compromise between entirely site-specific and globally-common parameter estimates. We applied the hierarchical method to annual data on organic matter loading and benthic oxygen demand from 34 estuarine and coastal systems. Both global and system-specific parameters were estimated using Bayes Theorem. Compared to the global model, the hierarchical model results in predictions of oxygen demand that more accurately represent site-specific observation but are less precise than the global model. Lower precision occurs because, by allowing each system to have its own parameter values, we effectively reduce the amount of information we have to estimate those parameters. However, if, by permitting model parameters to differ by location, the hierarchical model is believed to be more realistic than the global model, then the lower precision represents a more proper translation of our knowledge into predictions. Appropriate representation of prediction precision can have important implications for management intended to reduce oxygen depletion. Depending on the predictive precision resulting from the availability and nature of site-specific data, the hierarchical model may suggest more or less stringent organic matter loading rates than a model assuming global parameter commonality. The generality of the hierarchical approach makes it suitable for a number of ecological modeling applications in which cross-system data are required for empirical parameter estimation, yet only partial commonality can be assumed across sampling units.

Pro‐environmental behavior

The determinants of individual behaviors that provide shared environmental benefits are a longstanding theme in social science research. Alternative behavioral models yield markedly different predictions and policy recommendations. This paper reviews and compares the literatures from two disciplines that appear to be moving toward a degree of convergence. In social psychology, moral theories of pro‐environmental behavior have focused on the influence of personal moral norms while recognizing that external factors, such as costs and incentives, ultimately limit the strength of the norm‐behavior relationship. Rational choice models, such as the theory of planned behavior in social psychology and the theories of voluntary provision of public goods in economics, have sought to incorporate the effects of personal norms and to measure their importance in explaining behaviors, such as recycling and the demand for green products. This paper explores the relationship between these approaches and their implications for the theory and practice of ecological economics.

Stakeholder Values and Scientific Modeling in the Neuse River Watershed

In 1998, the North Carolina Legislature mandated a 30% reduction in the nitrogen loading in the Neuse River in an attempt to reduce undesirable environmental conditions in the lower river and estuary. Although sophisticated scientific models of the Neuse estuary exist, there is currently no study directly relating the nitrogen-reduction policy to the concerns of the estuarine systems stakeholders. Much of the difficulty lies in the fact that existing scientific models have biophysical outcome variables, such as dissolved oxygen, that are typically not directly meaningful to the public. In addition, stakeholders have concerns related to economics, modeling, implementation, and fairness that go beyond ecological outcomes. We describe a decision-analytic approach to modeling the Neuse River nutrient-management problem, focusing on linking scientific assessments to stakeholder objectives. The first step in the approach is elicitation and analysis of stakeholder concerns. The second step is construction of a probabilistic model that relates proposed management actions to attributes of interest to stakeholders. We discuss how the model can then be used by local decision makers as a tool for adaptive management of the Neuse River system. This discussion relates adaptive management to the notion of expected value of information and indicates a need for a comprehensive monitoring program to accompany implementation of the model. We conclude by acknowledging that a scientific model cannot appropriately address all the stakeholder concerns elicited, and we discuss how the remaining concerns may otherwise be considered in the policy process.

Robust Bayesian Uncertainty Analysis of Climate System Properties Using Markov Chain Monte Carlo Methods

A Bayesian uncertainty analysis of 12 parameters of the Bern2.5D climate model is presented. This includes an extensive sensitivity study with respect to the major statistical assumptions. Special attention is given to the parameter representing climate sensitivity. Using the framework of robust Bayesian analysis, the authors first define a nonparametric set of prior distributions for climate sensitivity S and then update the entire set according to Bayes’ theorem. The upper and lower probability that S lies above 4.5°C is calculated over the resulting set of posterior distributions. Furthermore, posterior distributions under different assumptions on the likelihood function are computed. The main characteristics of the marginal posterior distributions of climate sensitivity are quite robust with regard to statistical models of climate variability and observational error. However, the influence of prior assumptions on the tails of distributions is substantial considering the important political implications. Moreover, the authors find that ocean heat change data have a considerable potential to constrain climate sensitivity.

Does high forecast uncertainty preclude effective decision support

The uncertainty in the predictions of models for the behaviour of environmental systems is usually very large. In many cases the widths of the predictive probability distributions for outcomes of interest are significantly larger than the differences between the expected values of the outcomes across different policy alternatives. This seems to lead to a serious problem for model-based decision support because policy actions appear to have an insignificant effect on variables describing their consequences, relative to the predictive uncertainty. However, in some cases it is evident that some of the alternatives at least lead to changes in the desired direction. A formal analysis of this situation is made based on the dependence structure of the variables of interest across different policy alternatives. This analysis leads to the conclusion that the uncertainty in the difference of model predictions corresponding to different policies may be significantly smaller than the uncertainty in the predictions themselves. The knowledge about the uncertainty in this difference may be relevant information for the decision maker in addition to the information usually provided. The conceptual development is supplemented with a presentation of convenient methods for practical implementation. These are illustrated with a simple, didactical model for the effect of phosphorus discharge reduction alternatives on phosphorus loading to a lake.

Protein Aggregation Behavior Regulates Cyclin Transcript Localization and Cell-Cycle Control

Little is known about the active positioning of transcripts outside of embryogenesis or highly polarized cells. We show here that a specific G1 cyclin transcript is highly clustered in the cytoplasm of large multinucleate cells. This heterogeneous cyclin transcript localization results from aggregation of an RNA-binding protein, and deletion of a polyglutamine stretch in this protein results in random transcript localization. These multinucleate cells are remarkable in that nuclei cycle asynchronously despite sharing a common cytoplasm. Notably, randomization of cyclin transcript localization significantly diminishes nucleus-to-nucleus differences in the number of mRNAs and synchronizes cell-cycle timing. Thus, nonrandom cyclin transcript localization is important for cell-cycle timing control and arises due to polyQ-dependent behavior of an RNA-binding protein. There is a widespread association between polyQ expansions and RNA-binding motifs, suggesting that this is a broadly exploited mechanism to produce spatially variable transcripts and heterogeneous cell behaviors. PAPERCLIP: